Maximum likelihood models can be estimated from millions of sentences of bitext, but optimize a mismatched objective, predicting events observed in word aligned bitext instead of optimizing translation quality.

for each of these representation groups, all location groups (Between, Stack and Phrase) are employed.


To affect reordering, each sparse feature template is re-applied with each hypothesis extension.

given the appropriate tuning architecture, the sparse feature approach is actually simpler in many ways than the maximum entropy approach.


The impact of these reordering features is reduced slightly in the presence of more carefully tuned translation and language models, but they remain a strong contributor to translation quality.

there have not been nearly so many examples of helpful sparse features, especially for phrasebased systems.


The second type consists of a verb and an attached prepositional phrase, retaining only the head noun of the embedded noun phrase.

the New Event Detection task differs from our event recognition task because we want to find all stories describing a certain type of event, not just new events.


2 See Figure 1 for an example tagged sentence.

(12) can be decomposed into: where y ca and y ea respectively denotes Chinese and English named entity tags in a word alignment pair a. λ ycye = P (ycye) P (yc)P (ye) is the pointwise mutual information (PMI) score between a Chinese named entity tag y c and an English named entity tag y e .


Then we randomly sampled sentences from the collected sentences as Neg so that |Neg| was about twice as large as |Pos|: 5,000,000 for English, 1,400,000 for Japanese, and 600,000 for Chinese.

the parent dependency subtrees are adjacent to the candidate phrases and represented by their surface form (f 8,1 ), base form (f 8,2 ), or POS (f 8,3 ).


Proposed def is our method, which used TrDat for acquiring patterns (Section 2.1.2) and training.

the basic idea is actually language-independent.


Second, evaluating with Freebase held-out data is biased.

also notice that our models not only learn to predict Freebase relations, but also approximately 4k surface pattern relations.


By contrast, there columns are words, and rows are contextual features such as "words in a local window."

hence we also use various combinations, such as: Our models are parametrized through weights and latent component vectors.


Notice that we can accurately predict the Xscientist-at-Y surface pattern relation in table 2, as well as the more general person/company (em-ployedBy) relation in table 1.

for us even entailment rules are just a by-product of our goal to improve prediction, and it is this goal we directly optimize for and evaluate.


Our approach has a fundamentally different objective: we are not (primarily) interested in clusters of patterns or their semantic representation, but in predicting patterns where they are not observed.

our objective is to complete the matrix, whereas their objective is to learn better latent embeddings of words (which by themselves again cannot capture any sense of asymmetry).


This is a sign of either a large variance in usage or some data set specific tendency, and in either case we can not make confident claims as to this feature's association with any native language.

german speakers disprefer a prepositional phrase followed by a comma at the beginning of the sentence, and Chinese speakers use this pattern more frequently than the other L1s.


Future work could use these sequential cueing operations to investigate further claims of the dynamic recruitment hypothesis.

in any case, the source of this discrepancy presents an attractive target for future research.


McElree (2001; 2006) has found that retrieval of any non-focused (or in this case, unconnected) element from memory leads to slower processing.

by applying the F and L rules to the observed sign and context, the parser is able to generate a consequent context.


For example, the act of making breakfast may be interrupted by a phone call.

integrating two disjoint connected components should be expected to incur a processing cost due to the need to recall the current state of the superordinate sequence to continue the parse.


This research has been carried out in the framework of the TermWise Knowledge Platform (IOF-KP/09/001) funded by the Industrial Research Fund, KU Leuven, Belgium.

we record the lemmatized form when available, and the original form otherwise.


Including the baseline parsers, this gave us 24 parsers to evaluate on their respective test sets.

the oldest one is the talbanken or MAMBA treebank (Einarsson, 1976), which has later been reprocessed for modern use (Nilsson et al., 2005).


Again, we see that guided parsing is less effective if the guide uses an annotation style that is hard to parse.

additionally, we trained parsers using both methods at the same time; we refer to these parsers as combined.


the treebank annotation style) is different.

there is no point in trying to use domain adaptation methods assuming a covariate shift, e.g.


single pass over the data: in contrast, EM requires a few tens of passes (certainly more than 10 passes, from the results in table 1).

the algorithm takes two inputs in addition to the set of skeletal trees.


Collins (2003) reports an accuracy of 88.2 F 1 , which is comparable to the results in this paper.

to EM, the insideoutside algorithm is not required; however various operations such as calculating smoothing terms in the spectral method add some overhead.


The most suitable image is selected from this set using a graph-based algorithm which makes use of textual information from the metadata associated with each image and features extracted from the images themselves.

those approaches used queries that were much smaller (e.g.


Formally, φ t (the word distribution for tuple t) has a Dirichlet(ω ( t) ) prior, where for each word w in the vector,ω w is a log-linear function: (1) where ω (B) is a corpus-wide precision scalar (the bias), ω w is a corpus-specific bias for word w, and ω (k) t k w is a bias parameter for word w for component t k of the kth factor.

if the b value is near 0 for a particular triple, then it will have very low prior probability.


In addition to providing useful output for this important public health task, our prior-enriched model provides a framework for the application of f-LDA to other tasks.

this approach has limitations in that most documents are missing labels (less than a third of our corpus contains one of the labels in Table 1) and many messages discuss several components, not just the one implied by the tag.


We set this problem as a variant of the Textual Entailment (TE) recognition task (Mehdad et al., 2010b;Adler et al., 2012;Berant et al., 2011).

in this way, we can take advantage of the weights to make a more conservative decision in pruning the entailment chains.


In all cases, there is a significant improvement (p < 0.05) after applying the aggregation phase over the extracted phrases (Extrac-tion+Aggregation).

we only use TFxIDF (Salton and McGill, 1986), position of the first occurrence (Frank et al., 1999) and phrase length as our features.


Among the topic model based methods, TSM achieves the best results on all the three metrics.

as shown in Table 5, topic modelling based methods (i.e., Bayesseg, PLDa and TSM) outperform those using either TF or TF-IDF, which is consistent with previously reported results (Misra et al., 2009;Riedl and Biemann, 2012).


In this paper we take a generative approach lying between PLDA and SITS.

we present a new hierarchical Bayesian model for unsupervised topic segmentation.


This is different to other topic modelling approaches that run LDA as a precursor to a separate segmentation step (Misra et al., 2009;Riedl and Biemann, 2012).

instead of explicitly learning the segmentation, STMs just leverage the existing structure of documents from the given segmentation.


Acknowledgments Funded by NSF awards IIS-1218209 and IIS-0910611.

we performed a study to evaluate the agreement of the three metrics with human judgment.


Given K sequences of length N each, we can have O(N K) distinct words.

we must be able to estimate h pair (n) efficiently.


Our application is similar to automatic speech recognition in that there is a single correct output, as opposed to machine translation where many outputs can be equally correct.

due to the incremental nature of the algorithm and due to the lack of a principled objective function, it is not guaranteed to find the globally optimal alignment for the captions.


The performance of the model improves significantly as the WER reduces with adaptation.

the new features were y i z i , y i z i−1 and y i z i+1 .


It reflects a syntactic tendency of class-specific words to occur utterance-initially, which shows the feasibility of the online AD system.

we observe that the partial replacement of words with POS tags indeed improves over the baseline model performance, by 1.5 points on ASR output and by 1.1 points on transcripts.


This data can therefore be used for modeling H-C speech.

as reflected in Table 3, the H language model that leaves out the Fisher data actually performed better.


We address the goal of achieving a system that balances translation accuracy and latency.


We also experiment with inserting text segmenters of various types between ASR and MT in a series of real-time translation experiments.

constrained model adaptation (cMA) was applied to the warped features and the adapted features were recognized in the final pass with the VTLN model.


ϕ R which selects the first SF s ∈ S(v), such that C R (v, s) > 0 when traversing the trees T 1 .

in order to do so, we have compared the upper bound of the number of SF errors that can be corrected when using reranking and our approach.


Another early attempt (Tillmann and Zhang, 2006) used phrase pair and word features in a block SMT system trained using stochastic gradient descent for a convex loss function, but did not compare to MERT.

changing the weight drastically for a feature that is non-zero for only one out of a million sentences has very little effect on translation metrics.


An exception is the LSAspec from Kireyev (2009), based on latent semantic analysis (Deerwester et al., 1990), which is defined as the ratio of a term's LSA vector length to its document frequency and thus can be interpreted as the rate of vector length growth.

we use the set of all keywords for evaluation; otherwise a more complicated evaluation metrics for each dataset will be needed.


4 Applications We first investigate CTI in a well defined setting.

then we could test our metric based on other search engines such as Google or Bing.


Given a term t, define its universal context set U (t) = {c i } and the source of c i is S(c i ) = {d ij }.

for keyword extraction, a topic with a rich literature, to the best of our knowledge, has no publicly available large scale datasets, which makes SemEval2010 the best available.


The numbers above are interesting because they provide intrinsic evaluation of the concept induction procedure, but they do not tell us much about their usability.

note that the expriment is performed in three domains for which such translations are manually annotated.


The algorithm is executed a number of times (see Section 5.1 for parametrization of the algorithms) to learn all concepts in the set of summaries, and at each iteration a single concept is formed.

the obtained results were not assessed in a real information extraction scenario.


This dendrogram closely relates to the language groupings described in (Heine & Nurse, 2000).

the result of this grouping is usually illustrated as a dendrogram; a tree diagram used to illustrate the arrangement of the groups produced by a clustering algorithm (Heeringa & Gooskens, 2003), whereas multidimensional scaling adds to illustrate the visualization of the language proximities in a 2-dimensional space.


In the current research, we investigate the use of Levenshtein distance on orthographic transcriptions for the assessment of language similarities.

in conclusion, the paper discusses the results.


The rule counts are then used to compute labeling probabilities P (s | t) and P (t | s) over left-handside usages of each source label s and each target label t. These are simple maximum-likelihood estimates: if #(s i , t j ) represents the combined frequency counts of all rules with s i ::t j on the left-hand side, the source-given-target labeling probability is: t∈T #(s i ::t) The computation for target given source is analogous.

we can thus write the extracted rule as: while the SAMT label formats can be trivially converted into joint labels X::t, X::t 1 +t 2 , X::t 1 /t 2 , X::t 1 \t 2 , and X::X, they cannot be usefully fed into the label collapsing algorithm because the necessary conditional label probabilities are meaningless.


This division into 10 folds is done for reasons explained earlier in Section 2.1.

this is especially true for language pairs such as Urdu-English which have significantly different sentence structures.


Our aim is to go beyond this limitation of the TSP model and use a richer set of features instead of using pairwise features only.

a detailed ablation test shows that of all the features used, the pos triplet features are most informative for reordering.


These include Hierarchical models (Chiang, 2007) and syntax based models (Yamada and Knight, 2002;Galley et al., 2006;Liu et al., 2006;Zollmann and Venugopal, 2006).

we use machine aligned data in addition to manually aligned data for training the TSP model as it leads to better performance.


An explicit representation of the model would have required nearly a terabyte of memory, but its implicit representation using the parallel text required only a few gigabytes.

second, we propose novel data structures and algorithms for phrase extraction ( §4) and scoring ( §5) that are amenable to GPU par-allelization.


Note that in order to save space, the values stored in the arrays are sentence-relative positions (e.g., token count from the beginning of each sentence), so that we only need one byte per array entry.

finally, we arrive at line 7 in Algorithm 3, where we must compute feature values for each extracted phrase pair.


GPUs have previously been applied to DNA sequence matching using suffix trees (Schatz et al., 2007) and suffix arrays (Gharaibeh and Ripeanu, 2010).

this can be slow because on-demand extraction of phrase tables is computationally expensive.


Across different τ , we find that the first iteration provides most of the gain while the subsequent iterations provide additional, smaller gain with occassional performance degradation; thus the translation performance is not always monotonically increasing over iteration.

at 50% level of pruning, there is a loss about 0.


The most popular algorithm for this weight optimisation is the line-search based MERT (Och, 2003), but recently other algorithms that support more features, such as PRO (Hopkins and May, 2011) or MIRA-based algorithms (Watanabe et al., 2007;Chiang et al., 2008;Cherry and Foster, 2012), have been introduced.

whilst the differences between promix and perplexity minimisation are not large on the nc test set (about +0.5 BLEU) the results have been demonstrated to apply across many language pairs.


ELISSA uses a rule-based approach (with some statistical components) that relies on the existence of a DA morphological analyzer, a list of hand-written transfer rules, and DA-MSA dictionaries to create a mapping of DA to MSA words and construct a lattice of possible sentences.

the lack of standard orthographies for the dialects and their numerous varieties pose new challenges.


(2010), who use it as a feature to predict how well a parser will perform when applied across domains.

i have worked and continue to work in both of these areas, so i make this argument not as a criticism of others, but in a spirit of self-reflection.


(2001) proposed to replace non-standard words with "the contextually appropriate word or sequence of words."

twitter users in the USA contain an equal proportion of men and women, and a higher proportion of young adults and minorities than in the population as a whole (Smith and Brewer, 2012).


This prevents us from using very large minibatches.

we instead explore the idea of "minibatch" for online large-margin structured learning such as perceptron and MIRA.


On one hand, researchers have been developing modified learning algorithms that allow inexact search (Collins and Roark, 2004;Huang et al., 2012).

consider the Lagrangian: 2 Actually this relaxation is not necessary for the convergence proof.


First, except for the largest minibatch size of 48, minibatch learning generally improves the accuracy  of the converged model, which is explained by our intuition that optimization with a larger constraint set could improve the margin.

the communication overhead and update time are not included.


online learning for NLP typically involves expensive inference on each example for 10 or more passes over millions of examples, which often makes training too slow in practice; for example systems such as the popular (2nd-order) MST parser (McDonald and Pereira, 2006) usually require the order of days to train on the Treebank on a commodity machine (McDonald et al., 2010).


For example, consider the following sentences: (1) a.

an unlexicalised parser is also likely to be less biased to domains or genres manifested in the text used to train its original ranking model.


Experiments with a wide variety of distributional word similarity measures revealed that WeightedCosine (Rei, 2013), a directional similarity measure designed to better capture hyponymy relations, performed best.

unlexicalised parsers avoid using lexical information and select a syntactic analysis using only more general features, such as POS tags.


This leaves room for improvement in designing a system that can more easily adapt to previously unseen data.

this is largely due to the assumption that a domain-specific, tagged training set will not be available for most target domains.


In summary our contributions are: (a) We automatically construct a bilingual lexicon of NEs paired with the transliteration/translation decisions in two domains.

we construct a classification-based framework to automate this decision, evaluate our classifier both in the limited news and the diverse wikipedia domains, and achieve promising accuracy.


For tokens having Fr score between 0.5 and 0.6, the decision is not obvious.

we tackle this problem in the reverse direction (translating/transliterating English NEs into Arabic).


Phillies defeat Dodgers to take the National League Championship series.

this problem often arises in general multi-document summarization.


The CATiB filter also resolves some POS ambiguity given information in the CATiB POS tag.

every additional morphological filter has a positive impact and the improvement of the accuracy for full Buckwalter with each new filter ranged between 0.22% and 1.18% absolute except for the case filter, which adds almost 5%.


We apply our baselines, TADA, TADA+filters and TADA+filters+MLE to the blind test set (see Table 2).

we will present here a preliminary error analysis of TADA's output to motivate the morphological filters presented next (Section 4.3).


Our decoder for text normalization effectively integrates multiple normalization operations.

to avoid spurious candidates, we only generate w if |w| ≥ 3 and |w | − |w| ≤ 4.


We randomly add, delete, and substitute punctuation symbols in formal texts with equal probabilities.

also, fixing different types of informal characteristics often depends on each other.


In practice, this hypothesis producer can propose many spurious candidates w for an informal word w. As such, after we replace w by w in the hypothesis, we require that some 4-gram containing w and its surrounding words in the hypothesis appears in a formal corpus.

for example, given the un-normalized English test message "yeah must sign up , im in lt25", our English-Chinese MT system translated it into "对[yeah] 必须[must] 签 署[sign up] ， im 在[in] lt25" our normalization decoder normalized it into "yeah must sign up , i 'm in lt25 ."


A simple cross-validation approach can be used in case of very small data.

20}, and measured the perplexity of the data given to the model after convergence.


Both algorithms were implemented in Java, and the code for both is almost identical, except for the set of instructions which computes the dynamic programming equation for propagating the beliefs up in the tree.

• For each a ∈ N , we have a parameter π a , which is the probability of a being the root symbol of a derivation.


The minimal r required for an exact tensor decomposition can be smaller than m 2 .

in the probabilistic domain, approximation by means of regular grammars is also exploited by Eisner and Smith (2005), who filter long-distance dependencies on-the-fly.


(2011), we use Amazon's Mechanical Turk service to produce the first publicly available 1 dataset of negative deceptive opinion spam, containing 400 gold standard deceptive negative reviews of 20 popular Chicago hotels.


Second, the effect of deception on the pattern of pronoun frequency was not the same across positive and negative reviews.

in this section we discuss our efforts to extend Ott et al.


A two-tailed binomial test suggests that JUDGE 1 and JUDGE 2 both perform better than chance (p = 0.0002, 0.003, respectively), while JUDGE 3 fails to reject the null hypothesis of performing at-chance (p = 0.07).

first, as might be expected, negative emotion terms were more fre-quent, according to LIWC (Pennebaker et al., 2007), in our fake negative reviews than in the fake positive reviews.


In this work we distinguish between two kinds of deceptive opinion spam, depending on the sentiment expressed in the review.

we create and divide 400 HITs evenly across the 20 most popular hotels in Chicago, such that we obtain 20 reviews for each hotel.


The nonWikiRev systems perform inconsistently, heavily dependent on the characteristics of the test set in question.

firstly, a model trained directly on the corrections performs well across test sets.


For each language, we choose up to 8, 000 source language words among those that occur in the monolingual data at least three times and that have at least one translation in our dictionary.

to that work, we use a seed bilingual lexicon for supervision and multiple monolingual signals proposed in prior work.


The drop can be mostly explained by the fact that the two sentiment lexicons we use for evaluation are finite (i.e.

we found that the gradable adjectives are a proper subset of predicative adjectives, which is in line with the observation by (Bolinger, 1972, 21) that gradable adjectives (which he calls degree words) readily occur predicatively whereas nongradable ones tend not to.


Figure 3: A translation example of the base HPB system (above) and the system with constraints (below).

• Consistent with the conclusion in Koehn et al.


(2009; presented models that learn phrase boundaries from aligned dataset.

in this paper, we propose a two-level approach to exploiting predicate-argument structure reordering in a hierarchical phrase-based translation model.


• Flattening parse trees further improves 0.4∼0.5 BLEU points on average for systems with our syntactic constraints.

they only considered the reordering between arguments and their predicates.


Krippendorff (2004) recommends that an α of 0.8 is necessary to claim high-quality agreement, which is achieved by the MaxDiff methodology.

the unadjudicated agreement for the dataset was 67.3 measured using pair-wise agreement.


For example, the time for tagging one sentence in English NER was reduced from 5.6 ms to 1.6 ms, shown in Table 6.

it is also necessary to build compound embedding features since they can better deal with rarewords and ambiguous words.


Supervised learning methods have achieved great successes in the field of Natural Language Processing (NLP).

despite of this, same conclusions with chunking held.


Only users can give a rating about their satisfaction level, i.e., how they like the system and the interaction with the system.

2010derived turn level ratings from an overall score applied by the users after the dialogue.


(2011b) not only contains user ratings but also expert ratings which makes it a perfect candidate for our research presented in this paper.

in the data used for the experiments, the amount of occurrences of the ratings was not balanced (equal for all classes) which has been identified as the most likely reason for this effect.


We present alternative approaches to each question below.

to scale to large corpora, we propose a novel BAtCH DISSIMILARItY method.


For the larger corpora, BJAC reduces V max by over 50% compared to the baseline, and by 23% compared to Z&I.

training LVMs on massive corpora introduces computational challenges, in terms of both time and space complexity.


Nanba and Okumura (1999) came up with a simple schema composed of only three categories: Basis, Comparison, and other Other.

all the dependency relations that appear in the citation context.


To study the impact of using citation context in addition to the citing sentence on classification performance, we ran two polarity classification experiments.

takes a value of 1 if the current sentence starts with a conjunctive adverb (Furthermore, Accordingly, etc.


running the same experiments on new subjects.


For example a problem instance could be learning to distinguish articles about Macintosh and motorcycles MAC-MOTORCYCLES (evaluated on the 20 Newsgroups test section) using labeled data from IBM-BASEBALL (the training section).

meta-analysis is applicable to experiments with multiple datasets.


The lower MT scores and slower learning curve of the MTurk systems are both due to the lower quality of the translations, and to the mismatch with the professional development set translations (we discuss this issue further in §4.3).

we also show that adding a Mechanical Turk reference translation of the development set improves parameter tuning and output evaluation.


The four experimental systems have reordering models that are trained on the first 25,000 sentences of the parallel news data that have been parsed with each of the tree-to-dependency conversion schemes.

the number of relation types used in the conversion schemes proves important.


Neither the context-sensitive respeller nor dictionary lookup seem to contribute much to eSpeak's performance.

the model learns which n-grams are characteristic of ambiguous or unambiguous syllables.


The respellings designed by the author were much more effective for that purpose than either the IPA phonetic transcription or phonemic respelling (Section 4.3).

each of the examples 3-5 indicate the evaluators' acceptance of a particular respelling device: silent letters, multi-syllable units, and dictionary words.


We provide results showing our model is an order of magnitude faster to train than Model 4, that it requires no staged initialization, and that it produces alignments that lead to significantly better translation quality on downstream translation tasks ( §4).

last, generate the m output words, where each e i depends only on f a i .


, m], the alignment to the source and its translation is independent of all other translation and alignment decisions.

6 Second, using a 10k sample of the French-English data set (only 0.5% of the corpus), we determined 1) whether p 0 should be optimized; 2) what the optimal Dirichlet parameters µ i are; and 3) whether the commonly used "staged initialization" procedure (in which Model 1 parameters are used to initialize Model 2, etc.)


For Arabic-to-English, the bilingual data consists of roughly 100K sentences of in-domain TED talks data and 8M sentences of "other"-domain United Nations (UN) data.

in addition to the phrase based decoder, Jane 2.0 implements the forced alignment procedure used in this work for the purpose of adaptation.


In that case, the chance of finding a correct translation is reduced (Pekar et al., 2006).

in general if a query term has a low-frequency in the corpus, then its context vector is sparse.


POS tagging is the problem of assigning syntactic categories or POS to tokenized word forms in running text.

the EWt contains development and evaluation data for five domains: answers (from Yahoo!


The structured perceptron is similar to the averaged perceptron (Freund and Schapire, 1999), except data points are sequences of vectors rather than just vectors.

this section provides some intuition for using inverse Zipfian distributions as weight functions.


This was also the training data used in the experiments in the Parsing the Web (PTW) shared task at NAACL 2012.

performance of state-of-the-art supervised systems is known to drop considerably on out-ofdomain data.


1-ORCL Select the best performing attribute on the test set.

these algorithms use examples from each domain to learn a general model that is also sensitive to individual domain differences.


Measures of semantic and pragmatic atypicality in spontaneous language are rarely directly measured.

in addition, we report the results of applying these word ranking techniques in combination with the two filtering techniques.


We then apply word ranking methods and distributional semantic modeling to these narrative retellings in order to automatically identify these unexpected words.

words with high tf-idf and log odds scores are likely to be those unrelated to the topic of the NNM story.


The participants in that study were chosen to reflect the demographics of adults in the United States; thus, speakers of varying reading levels and nonnative speakers were included.

we investigate finegrained measures that, if useful in identifying points of difficulty for readers, can lead to new approaches for assessing text difficulty.


They observe half of the WER reduction that the fully supervised methods achieve.

a closer look reveals that the syllable-based CM paired with NO-LM is an outlier because NO-LM approach allows variety at the output but when the unit of the confusion model is as small as syllables, it produces too much variety that deterio-rates the discriminative model.


The eye movement paths available from the screen recordings done during sense annotation conform to this theory.

these approaches can be classified as weak AI systems.


We note that this measure is qualitatively similar to relational similarity model of (Turney, 2012), which predicts similarity between members of the word pairs To evaluate the vector offset method, we used vectors generated by the RNN toolkit of Mikolov (2012).

also of note, the use of distributed topic representations has been studied in (Hinton and Salakhutdinov, 2006;Hinton and Salakhutdinov, 2010), and (Bordes et al., 2012) presents a semantically driven method for obtaining word representations.


It is clear that even with a simple weighing approach, the PPDB scores show a clear correlation with human judgements.

here, we obtain S → NP expect S, for which PPDB has matching rules like S → NP expect S | NP would hope S, and S → NP expect S | NP trust S. This allows us to apply sophisticated paraphrases to the predicate while capturing its arguments in a generalized fashion.


4 The resulting composite parallel corpus has more than 106 million sentence pairs, over 2 billion English words, and spans 22 pivot languages.

they can be used to bias the collection towards greater recall or higher precision.


We compare our proposed method based on hitting times (HT) with two variants of iterative bootstrapping.

the goal of relation extraction is to extract tuples of a particular relation from a corpus of natural language text.


In this paper, we show that a significant number of "negative" examples generated by the labeling process are false negatives because the knowledge base is incomplete.

in the M-step, we retrain both of the mentionlevel and the aggregation level classifiers.


Figure 3 gives common and characteristic student responses for each setup on a question for which Dictionary Words differed significantly.

in this study we consider only the baseline, non-adaptive conditions of those experiments.


We report results from 5-fold cross validation performed on the entire corpus.

now all three other classes also improve their performance, and we obtain a 10.6% error reduction on overall accuracy over the baseline system.


A baseline system for this task would simply assign the majority class (high-coherent) to all of the responses; this baseline achieves an F-Measure of 0.587.

we generated there basic entity grids: EG_SOX (entity grid with the syntactic roles S, O, and X), EG_REDUCED (entity grid with the reduced representations P and N), and EG_SALIENT (entity grid with salient and non-salient entities).


Recently, Yannakoudakis and Briscoe (2012) systematically analyzed a variety of coherence modeling methods within the framework of an automated assessment system for non-native free text responses and indicated that features based on Incremental Semantic Analysis (ISA), local histograms of words, the part-ofspeech IBM model, and word length were the most effective.

for coherence modeling, we again use the J48 decision tree from the Weka machine learning toolkit (Hall et al., 2009) and run 4-fold crossvalidation on the 600 annotated responses.


Based on the above analysis, we plan to investigate additional superficial features explicitly related to discourse coherence, such as the distribution of conjunctions, pronouns, and discourse connectives.

we adopt the weighted average F-Measure to evaluate the performance of coherence prediction: first, the F1-Measure of each category is calculated, and then the percentages of responses in each category are used as weights to obtain the final weighted average F-Measure.


We then run an iterative process where in each iteration we update both Λ and θ for each training sample.

as discussed in §3.1 this metric is exact, indicating whether the generated regular expression is semantically equivalent to the correct regular expression.


The n-best parser always represents the n-best parses, which is set to 10,000 in our experiments.

at each iteration we find the n-best parses with the current lexicon, and find the subset of these parses which are correct using DFa equivalence.


We do this using a modified version of Møller (2010).

this information is augmented with a (/) or a (\) for each argument indicating whether that argument comes from the left or the right, in sentence order.


Given a document D and a word w in D, Z w = (f, e) represents an assignment of w to frame f ∈ F and frame element e ∈ E f ∪ S f .

we extend PROFINDER to leverage this intuition by incorporating a "stickiness" prior (Haghighi and Vanderwende, 2009) to encourage neighboring clauses to stay in the same frame.


The BNC comprises 4,049 texts totalling approximately 100 million words.

the different senses of a word "exist in parallel" until it is observed in some context.


In isolation, the word pen may refer to a writing implement, an enclosure for confining livestock, a playpen, a penitentiary or a female swan.

although guppy is an example of a pet-fish it is neither a very typical pet nor fish (Osherson and Smith, 1981).


A word's usage is learned from the type of dependency relations it has with its immediate neighbors in dependency graphs.

their product cc * = (r e iθ )(r e −iθ ) = r 2 is real.


Table 5 reports F1 scores on both the positive and negative examples of TEST.

from line 2 on, the first column is the reference output and the second column is the model output with the marginal probability for predicated labels.


The approach explores both trees in a bottom-up, postorder manner, running in time: where |T i | is the number of nodes, D i is the depth, and L i is the number of leaves, with respect to tree T i .

we show that this approach is better formulated as a (strongly indicative) feature of a larger set of answer extraction signals.


2012present an improved system called OLLIE, which relaxes the previous systems' constraints that relation words are mediated by verbs, or relation words that appear between two entities.

it is often difficult to determine which textual fragments to extract.


With these notations, RD(q, s) is true if and only if is true for some w h , w d |q , for some u h , u d |s and for some i and j. EQ(w h , u h )∧EQ(w d , u d ) requires that the question dependency w h , w d |q and the snippet dependency u h , u d |s match; w h ∈ m (i) ∧ w d ∈ m (j) requires that the head word and dependent word are in the i th -rank and j th rank MMP, respectively.

these features are designed to characterize a phrase from the lexical, syntactic, semantic and corpus-level aspect.


The ultimate goal of the MMP model is to improve the performance of our question-answering system.

rD(q, s) is a dependency feature enhanced with MMPs.


This merged graph becomes a new history graph for the next utterance.

only nouns are considered as potential keywords.


The keywords of the current utterance are extracted by TextRank (Mihalcea and Tarau, 2004) from the merged graph of the current utterance and the history graphs.

there have been a few studies focused on keyword extraction from spoken genres.


We assume that only the preceding utterances that are directly related with the current utterance are important for extracting keywords from the current utterance.

this is collaborative approach to extract keywords in a document.


The frequency-based keyword extraction with TFIDF weighting (Frank et al., 1999) and the graph-based keyword extraction (Mihalcea and Tarau, 2004) are two base models for this task.

it would be helpful to meeting participants to provide them with some additional information related to the current subject.


In this study, they considered prosodic information from HKT forced alignment and topics in a lecture generated by Probabilistic Latent Semantic Analysis (pLSA).

they are not suitable for meeting transcripts.


For anaphor m i and its antecedent candidates E m i (e ij ∈ E m i ), the numeric score for pair {m i , e ik } is S ik .

based on (1) and (2), MLNs allow us to express relations between anaphor-anaphor and anaphor-antecedent pairs ((m,n) or (m,e)) on the global discourse level improving accuracy by performing joint inference.


MLNs are a powerful representation for joint inference with uncertainty.

work on implicit noun roles is mostly focused on few predicates (e.g.


It makes strong untested assumptions about bridging anaphora types or relations, limiting it to definite NPs (Poesio and Vieira, 1998;Poesio et al., 2004;Lassalle and Denis, 2011) or to part-of relations between anaphor and antecedent (Poesio et al., 2004;Lassalle and Denis, 2011).

entity coherence can rely on more complex, lexico-semantic, frame or encyclopedic relations than identity.


Even an unsupervised model based on these constraints provides substantial gains over feature-based models for most AZ categories.

recent work has shown that explicit declaration of domain and expert knowledge can be highly useful for structured NLP tasks such as parsing, POS tagging and information extraction (Chang et al., 2007;Ganchev et al., 2010).


Many SMT systems, including our own, still use this distance-based penalty as a feature.

the isi corpus consists of comparable data: sentence pairs whose source-and target-language sides are similar, but often not mutual translations.


We show a reference translation of the Chinese source (not found in the comparable data) that reorders the phrases as 1, 3, 2.

we also tried augmenting this with separate log-linear features corresponding to subcorpus-specific RMs.


For a given phrase pair (f , e), we estimate the probabilities that it will be in an M, S, or D orientation o with respect to the previous phrase pair and the following phrase pair (two separate distributions).

since in Cantonese, "verb adverb" is a more common word order, speakers and writers of Mandarin in Hong Kong may adopt the 012 34 135 013517 2 38 9 34 2 8 2 7 013517 2 38 9 "verb adverb" order in that language as well.


We introduce four methods based on the above formulation and each method uses a different type of g(•) function for combining different metrics and we compare experimentally with existing methods.

our method is able to combine multiple metrics (each of which compares to the reference) during the tuning step and we do not depend on N-best list (or forest) rescoring or system combination.


Or the user can encode her beliefs or expectations about the individual solutions {p s 1 , .

due to the scaling differences between the scores of different metrics, the linear combination might completely suppress the metric having scores in the lower-range.


Using standard finite state algorithms, they intersect the two automatons then exactly search for the highestscoring paths.

referencing vertex v becomes partial edge "is ( )[0 + ] ."


The alignment models proposed mostly follow the original generative stories while introducing additional phrasal conditioning into models 3 and 4.

in addition to sampling the alignments, we also place a uniform Beta prior on the discount parameters and a vague Gamma prior on the strength parameters, and sample them using slice sampling (Neal, 2003).


The most basic word alignment model, IBM model 1, can be described using the following generative process (Brown et al., 1993): Given an English sentence E = e 1 , ..., e l , first choose a length m for the foreign sentence F .

we place the following assumptions on IBM model 1: In this probability modelling we assume that the alignment positions are determined using the uniform distribution, and that word translations are generated depending on the source word -the probability of translating to a specific foreign word depends on the observed frequency of pairs of the foreign word and the given source word.


The system performance was then evaluated against these judgements in terms of precision (P ), i.e.

one of the first attempts to identify and interpret metaphorical expressions in text is the met* system of Fass (1991), that utilizes hand-coded knowledge and detects non-literalness via selectional preference violation.


The clusters that have no member noun were hidden from the ranking since they do not explicitly represent any concept.

the matrix B denotes the n × m adjacency matrix, with b ip being the connection weight between the vertex v i and the cluster u p .


We calculated system precision (in all experiments) as an average over both annotations.

not only HGFC outperformed agglomerative clustering methods in hi-erarchical clustering tasks (Yu et al., 2006;Sun and Korhonen, 2011), but its hierarchical graph output is also a more suitable representation of the concept graph.


The final parameter values for the other methods, found by exhaustive search, are summarized in Table 1.

a major drawback of this strategy is the dependency on the coverage of the resource, which has a direct impact on the lexical chains.


Statistical methods to modeling language semantics have proven to deliver good results in many natural language processing applications.

it can be concluded that there is a moderate to high agreement regarding the annotator selections of candidate terms, which is ensured by preselection of candidate terms by part-of-speech patterns.


The statistics of our labeled examples are presented in Table 2.

our definition of entities depends on the given knowledge base, rather than human judgment.


Ideally, we would like to manually label all the posts to obtain the ground truth for evaluation.

the polarity of the interaction expression in the post is dependent on the viewpoint y u,n and the viewpoints of the previous post(s).


For our task, we take a simpler approach and use a sentiment lexicon together with some heuristics to predict the polarity of interaction expressions.

jVTM: The model is shown in Figure 3(a), a variant of jVTM-UI that does not consider user interaction.


Note that FS-EM1 and FS-EM2 work slightly better than Co-Class in domain "Camera" because it is the least noisy domain with very short posts while other domains (as source data) are quite noisy.

based on the discussion above, the key to solve the problem of EM is to find a way to reflect the features in the target domain during the iterations.


One can conceive a two-pass scenario where we first make a binary decision of whether there is an empty category associated with the head in the first pass and then determine whether there is an EC associated with the tuple as well as the EC type in the second pass.

dev Test 81-325, 400-454, 500-554 41-80 1-40 590-596, 600-885, 900 901-931 As discussed in Section 2, the gold standard dependency structure parses are converted from the CTB parse trees, with the ECs preserved.


As a result, current semantic role labeling systems can only recover explicit arguments.

this context can provide discriminative clues that may help identify the types of empty category.


We also introduce a variety of new features that are more suited for this approach.

our model did poorly on dropped pronouns (*pro*).


Methods for learning with ambiguous labelings have previously been proposed in the context of multi-class classification (Jin and Ghahramani, 2002), sequence-labeling (Dredze et al., 2009), log-linear LFG parsing (Riezler et al., 2002), as well as for discriminative reranking of generative constituency parsers (Charniak and Johnson, 2005).

figure 3 outlines an example of how (and why) AAST works.


AAST, on the other hand, achieves consistent gains, rising from 62.0% to 64.0% on average.

for languages 3 Model "D-,To" in Table 2 from Naseem et al.


Using this task and a model of the meaning of spatial language, we next discuss two agents that play the game: ListenerBot (Section 4) makes decisions using a single-agent POMDP that does not take into account the beliefs or actions of its partner, whereas DialogBot (Section 5) maintains a model of its partner's beliefs.

since this behavior confuses the other agent and thus has a lower utility, it gets replaced by truthful communication as the policies improve.


Prediction can support specific response capabilities, such as system completion of user utterances (DeVault et al., 2011a) and reduced response latency.

the analysis in this paper has explored a method of approximating explicit incremental NLU using predictive techniques in finite semantic domains.


The algorithm takes an ambiguous word w as input, and outputs its corresponding similarity-based pseudoword P w whose i th pseudosense models the i th sense of w, together with a confidence score which we detail below.

given a pseudoword p and an untagged corpus C, this artificial tagging is achieved by substituting all occurrences of w i in C with p for each pseudosense i ∈ {1, .


The score, however, remains above 0.70 with highlypolysemous pseudowords.

as our WSD system for this experiment, we used It Makes Sense (IMS), a state-of-the-art supervised WSD system (Zhong and Ng, 2010).


Clearly, a sufficiently large sense-tagged corpus is required for calculating the occurrence frequency of the individual senses of a word.

constructing a pseudoword by merely combining a random set of unambiguous words selected on the basis of their falling in the same range of occurrence frequency (Schütze, 1992), or leveraging homophones and OCR ambiguities (Yarowsky, 1993), does not provide a suitable model of a real polysemous word (Gaustad, 2001;Nakov and Hearst, 2003).


A fundamental problem in computational linguistics is the paucity of manually annotated data, such as part-of-speech tagged sentences, treebanks, and logical forms, which exist only for few languages (Ide et al., 2010).

as mentioned earlier in Section 2, constructing a pseudoword by combining a random set of unambiguous words, as was done in these early works, can not model systematic polysemy (Gaustad, 2001;Nakov and Hearst, 2003), since different senses of a real ambiguous word, unless it is homonymous, share some semantic or pragmatic relation.


Under these conditions, setting different priors to reflect the annotator pool should improve performance.

whenever one label is more prevalent (a common case in NLP tasks), κ overestimates the effect of chance agreement (Feinstein and Cicchetti, 1990) and penalizes disproportionately.


We thus also implement Variational-Bayes (VB) training with symmetric Beta priors on θ j and symmetric Dirichlet priors on the strategy parameters, ξ j .

all interannotator agreement measures suffer from an even more fundamental problem: removing/ignoring annotators with low agreement will always improve the overall score, irrespective of the quality of their annotations.


For instance, "bright" and "intelligent" are frequently occurring in comma-separated enumerations, and "intelligent" fits well in the target context based on n-gram probabilities.

due to high annotation costs, methods that do not require labeled training data per target scale better to a large vocabulary.


In our view, the latter option provides a better assessment of the model's similarity judgements, since contextualizing low-similarity landmarks often yields non-sensical phrases (e.g.

the first step of our method consists in the construction of a latent factor model for nouns, based on their context words.


In general, we found the "MM" approach can perform better since it inherently incorporates both the "burstiness" and "lexical cohesiveness" of the event tweets, while the "Spike" approach relies solely on the "burstiness" property.

most previous summarization studies focus on the well-formatted news documents, as driven by the annual DUC 2 and TAC 3 evaluations.


A multi-document extension of RST is Cross-document Structure Theory (CST), which has been applied to MDS (Zhang et al., 2002;Jorge and Pardo, 2010).

g-FLOW only scores significantly lower than LIN and the gold standard summaries.


Furthermore, base forms and inflected forms separated by spaces, hyphens, or colons were discarded.

these modeling choices are directly inspired by the data setting: Wiktionary contains complete inflection tables for many lexical items in each of a large number of languages, so it is natural to make full use of this information with a joint model of all inflected forms.


In the alignment step, we minimize the edit distance between each inflected form and the base form to identify changed spans.

we find that regularization is effective at balancing high model capacity with generalization, and reducing the size of the feature set empirically harms overall accuracy.


In the former, the authors develop an active learning word selection strategy for inducing pronunciation rules.

our selection methods are fast, can select any number of data points in a single step, and are not tied to a particular prediction task or model.


Lemma Considering the amount of effort put in developing the guesser, the baseline POS tagging accuracy is relatively good.

our morphological analyzer is identical to the one used in the previous sections.


In fact, our model induces at most 4-6 roles (even if |R| is much larger).

namely, instead of computing an expectation of p(a i |a −i , r,v,C,u) under p(r|x, w), as in (3), we use the posterior distributions µ is = p(r i = s|x, w) and score the argument predictions as where µ are the posteriors for all the arguments, and φ i (a, a −i ) is the score associated with predicting lemma a for the argument i.


While each of the joint factors all improve over the baselines on RF, the full model with all the joint factors does not perform as well as with some factors excluded.

this differs from our model which is built on non-greedy joint inference, but much of the signal indicating when two mentions corefer or are aligned is similar.


For a thematic fit task, the correlation between calculated estimates and human judgements can be expected to improve.

we make use of the SDDM , SDDMX , and TypeDM tensors in our experiments to demonstrate how our techniques improve performance in thematic fit modelling across different feature spaces.


The main drawback of the V RC method is that it cannot evaluate fewer than three clusters, due to having both a V RC k+1 and a V RC k−1 term in Equation (5).

yet, this method poses a few theoretical questions.


We make use of the SDDM , SDDMX , and TypeDM tensors in our experiments to demonstrate how our techniques improve performance in thematic fit modelling across different feature spaces.

good role fillers that are very different from one another and belong to different senses of a verb can all be assigned thematic fit scores as high as those of good role fillers of monosemous verbs.


For example, if the correct phrase is always ranked 2, 50 or 100 out of list of 4600, median rank accuracy would be 99.95, 98.91 or 97.83.

we find that the additional composition constraint used in CNNSE has maintained the interpretability of the learned latent space.


4 HillClimbPOS: After sampling the initial values s, t (0) and y (0) , the hill-climbing algorithm improves the solution via locally greedy changes.

we obtain a 2.1% TedEval gain against the best published results in the 2013 SPMRL shared task (Seddah et al., 2013).


We observe that most sentences converge quickly.

the model samples the full path from the lattice, which corresponds to a valid segmentation and POS tagging assignment.


As these parsers employ a bottom-up chart-parsing strategy and use normal-form CCGbank derivations which are rightbranching, they are not incremental in nature.

a related secondorder matching-based mechanism was used by (Kwiatkowski et al., 2010) to decompose logical forms for semantic parser induction.


Note, for testing our system, we still need to evaluate every possible candidate report-source pair -that is ∼12,265 candidate sources per tested report.

topicBlock (Ho et al., 2012) models citation prediction with a hierarchical topic model but only uses the first 200 words of each document's abstract.


Of the past research which uses generative models for citation prediction, we believe LinkLDA is the only other system in which a source's prior citation probability plays any role in training the model.

we suspect that PrevCit-edSource was a good feature because our corpus was sufficiently large; had our corpus been much smaller, there might not have been enough data for this feature to provide any benefit.


The above ten categories are intended to be disjoint, so that a character n-gram belongs to exactly one of the categories.

which character n-grams are more like bag-of-words features (which tend to track topics), and which are more like stylistic features (which tend to track authors)?


• There aren't even any dead people on it, since by the very act of being dead and still famous, they assert their long-term impact.

we leave this research question for future work.


The parsing process is modeled as an application of a sequence of actions, transducing the initial state into a final state, while constructing de-  Table 1: arc-standard transition action sequence for parsing the sentence in Figure 2. pendency arcs.

the 128-beam system does not improve the performance significantly (48.2 vs 47.5), but runs twice slower.


Our choice of this statistical procedure has been informed by (Moore, 2004).

3 while we have presented signi cant improvements using additional constraints, one may won5even when caching feature extraction during training mcdonald et al (2005a) still takes approximately 10 minutes to train.


The significance level used to qualify a word as a keyword thus requires correction for multiple tests to reduce type I errors.

a traditional unigram language model is constructed using the actual term frequencies in the document, the resulting model capturing generative probabilities.


Furthermore, a news article is considered large if it has more than 300 tokens, which corresponds to the average number of words per article in our training set.

we decide to use as metrics ROUGE-1, ROUGE-2, and ROUGE-SU.


A potential confound for using this analysis as a proxy for the quality of the alignment model is that the ASR transcript is generally an ungrammatical sentence fragment as opposed to the grammatical recipe steps, which is likely to reduce the raters' approval of ASR captions in the case when both accurately describe the scene.

we cannot use keyword spotting if the goal is to align instructional text to videos.


For each recipe, we apply a suite of in-house NLP tools, similar to the Stanford Core NLP pipeline.

there are several pieces of related work.


To partially combat this problem, we used computer vision to refine candidate video segments as follows.

we first align the instructional steps to the speech signal using an HMM, and then refine this alignment by using a state of the art computer vision system.


There is by now a large literature on multimodal distributional semantic models.

we thank Adam Liska, Tomas Mikolov, the reviewers and the NIPS 2014 Learning Semantics audience.


We iteratively scan through our dataset, one protocol and video pair (x i , y i ) at a time.

the highest weighted features include: (write, pen), (aspirate, pipette), which agree with our intuition.


The feature weights learned by LSSVM and its variants were smaller than that for LSP (due to regularization).

the feature weights learned by LSSVM and its variants were smaller than that for LSP (due to regularization).


By constraining the alignment to the forced alignment, we avoid aggressive updates, which may have helped LSP-C and LSSVM-C to learn better alignments.

for LCRf, we sum over all the latent variables for estimating the expectations.


our goal, then, is to set θ so that the predicted label distribution matchesp j , for all j.


First, we consider c and m as a single sentence and compute a single bag-of-words representation b cm ∈ R V .

for the translation probabilities, we built a very large phrase table of 160.7 million entries by first filtering out Twitterisms (e.g., long sequences of vowels, hashtags), and then selecting candidate phrase pairs using fisher's exact test (Ritter et al., 2011).


Table 6 provides examples of system output.

• Portmanteau building occurs at the phoneme level.


Both systems are rule-based, rather than data-driven, and do not train or test their systems with real-world portmanteaux.

we also plan to research other applications for multi-input/output models.


Most disagreements between annotators are confusions between 'partial' and 'good partial' matches.

applying our method on a large dataset yields high quality sentence alignments that would benefit data-driven learning in text simplification.


In this paper, we propose to disambiguate NEs using a Personalized PageRank (PPR)-based random walk algorithm.

the coherence of the node e to the graph G quantifies how well node e "fits" into this graph.


We represent possible EC positions using the word embeddings of their contexts and then map them to a low dimension space for EC detection.

the problem of EC detection can be formulated as a classification problem: for each "head word, following word" pair, what is the type of the EC?


Incremental RR has also been studied in a number of papers, including a framework for fast incremental interpretation (Schuler et al., 2009), a Bayesian filtering model approach that was sensitive to disfluencies , a model that used Markov Logic Networks to resolve objects on a screen , a model of RR and incremental feedback (Traum et al., 2012), and an approach that used a semantic representation to refer to objects (Peldszus et al., 2012;Kennington et al., 2014).

(2011) used 14 task-specific features, three of which they found to be the most informative in their model.


We modeled the structured data extraction task as text categorization and NER tasks and applied machine learning (SVM) on the automatically generated training datasets.

a listing can have one or more of the following property types: retail, office, industrial, land, multi-family.


Lastly, it should be noted that an overall system performance baseline is one that measures the average performance of data entry staff in commercial real estate listing services.

the construction of listing data (for comparison with manually entered data) resulted in a strict performance measure.


Figure 6 shows that working with the smaller, context-specific sets dramatically decreases the model's ability to recover deleted segments.

in all cases, the model recovers far more underlying forms than it finds nonzero weights.


In addition, if u ∈ S and some phonological alternation p ∈ P maps u to a surface form s ∈ p(u) ∈ S, then (s, u) ∈ X .

we used the Buckeye underlying forms as our underlying forms.


We present results suggesting that these constraints simplify the search problem that the learner faces.

we found that the choice of L 1 versus L 2 regression makes little difference, and the model is insensitive to the value of the regulariser constant λ (we set to λ = 1 in the experiments below).


Hence, adding continuous-space representations of words can provide valuable information to the classifier and the classifier can learn better discriminative criteria based on such information.

the manually annotated instances (CC) include samples with this tag and therefore IMS + CC is able to associate a target word with this sense tag.


The alternations of each feature value can be straightforwardly interpreted as the birth and death (Le Quesne, 1974) of a lexical item.

then the posterior hyperparameters are α n = α+n/2 and the posterior predictive distribution is Student's t-distribution (Murphy, 2007): , where M hist is a collection of α, β and a history of previously observed differences.


(2008) proposed a generative model for hierarchical clustering, which straightforwardly explains evolutionary history.

the acceptance probability is where children(h ′ ) is the set of the target node's children.


We also conducted experiments on the test set by replacing the parsed graph with gold 1 A script to create the train/dev/test partitions is available at the following URL: http://goo.gl/vA32iI 2 Specifically we used CoreNLP toolkit v3.3.1 and parser model wsjPCFG.ser.gz trained on the WSJ treebank sections 02-21. relation labels or/and gold concept labels.

since this action will be applied to every node which is kept in the final parsed graph, concept labeling could be done simultaneously through this action.


See Gritzmann and Sturmfels (1992) Each vertex h i corresponds to a single index vector i, which itself corresponds to a single set of selected hypotheses.

the corresponding decision boundaries in their normal fans have also been drawn with dashed lines.


As already mentioned above, the size of the training corpus strongly affects the results.

following the idea presented in (Ballesteros et al., 2014b), a separate SVM-classifier is defined for the mapping of each linguistic category.


In particular, as one ages they think less about the immediate present and more about the future (Friedman, 2000;Nurmi, 2005;Steinberg et al., 2009), and females tend to think a bit more about the future than males (Keough et al., 1999).

features 3 available here: wwbp.org/public data/happierfuntokenizing.zip are encoded simply as binary indicators for whether the ngram appears in the message.


Other studies have established consistent links between temporal orientation and demographic characteristics.

unlike other areas of natural language processing where stochastic techniques dominate, rule-based systems have been quite competitive in time expressions recognition, especially in less domain dependent settings or for relaxed matching tasks (uzZaman et al., 2013).


We controlled for individual differences and date effects (e.g.

self report questionnaires are often used for convenience, not necessarily because they are most valid (Paulhus and Vazire, 2007).


Entities were labeled as one of three classes (person, location, or organization), and two entities were only considered a match if they both selected the same entity and the same entity class.

in general, the results suggest that a complex task such as dependency parsing suffers substantially when the input data differs from formal text in any number of ways.


Some work has chosen to focus on specific aspects of the normalization process, such as providing good coverage (Liu et al., 2012) or building normalization dictionaries (Han et al., 2012).

the per-token error rate highlights the cost of failing to perform a single instance of a given normalization edit, independent of the frequency of the edit.


She is trying to keep gay people out of marriage and thus preserve her heterosexual privledge.

are you saying you would be in favor of foregoing aLL the legal rights and benefits you are afforded by marriage?


Both Arg1 and Arg2 in Row 10 makes the same argument but Arg1 includes additional argumentation.

our subsequent impression was that the clustering had not filtered out enough of the unrelated pairs (score 0-1).


We used 10, 875, 982 freely available abstracts (not full text articles) from PubMed as our corpus.

they re- port a best F1 score of 48% but note that such score does not seem to reflect the quality of the clustering.


In order to evaluate the automatic clustering procedure that uses K-means++ and word vectors, we start with the gold standard provided by de Melo and Bansal (2013): as mentioned above, their data set has 88 gold standard clusters, corresponding to 346 adjectives, annotated by humans for scale ordering.

this experiment uses hand-corrected WordNet dumbbells to determine adjectives on the same scale of semantic intensity, followed by the MILP using strength counts from the Google N-gram corpus, to determine the ranking.


We used CBOW vectors to perform clustering and derived k = 300 and d = 250 using the approach described in Section 5.

the skip-gram model predicts the neighboring words given the current word.


Despite this, their unsupervised model only agrees with their supervised model on 55% of zero pronoun antecedents, suggesting that this hypothesis is weak.

figure 1 shows an example conversation in which zero pronouns are frequently used to refer to speaker or listener, and would be translated to English as "I" or "you."


First, we describe the data we use for experimentation.

at that point, the focus remains on S for several utterances until "The last round.


, c K }, automatically generate a visual paraphrase (i i , c i , p i ) for each (i i , c i ); then rerank the candidate captions by the following affinity function that merges the visual neighborhood from the  paraphrase, 6 Experiments: Visual Paraphrasing Improves Image Captioning The experimental configuration basically follows §4.

as a reference, the first row shows the performance of the INSTaNCE method ( §4).


The results show that the global training objective achieves best scores on both MAP and GAP for classifiers and lowdimensional embedding models.

one of the main drawbacks in existing KBs is that they are incomplete and are missing important facts (West et al., 2014), jeopardizing their usefulness in downstream tasks such as question answering.


We find that two of these are best suited for robust tagging.

we conclude that LM-based representations are more suited for tagging as they can be induced faster, are smaller and give better results.


PDT and MTE have been annotated using two different guidelines that without further annotation effort could only be merged by reducing them to a common subset.

11 We conclude that systems based on the algorithm of Martin et al.


Nor is the device necessary to represent the pronunciation of the preceding vowel; for example, SoundSpel has those words as 'maek' and 'maeking'.

for example, if the stem ends in a tt and the affix begins with an i, the consonant doubling rule implies that the free form of the morpheme ends in a single t, as in getting.


Since we have been been unsuccessful in finding such a lexicon, we extract the necessary information from two different resources: the CELEX lexical database (Baayen et al., 1995), which includes morphological analysis of words, and the Combilex speech lexicon (Richmond et al., 2009), which contains high-quality phonemic transcriptions.

the orthography of Serbo-Croatian was originally created according to the rule "write as you speak", so that the spelling can be unambiguously produced from pronunciation.


If it turns out that the word belongs to a positive sentiment class, then its topic distribution is drawn from a biased Dirichlet prior φ (1) We set ω w = 1 if the word w is a positive seed word, otherwise, we set ω w = 0.

then, we remove non-alphabet characters, numbers, pronoun, punctuation and stop words from the text.


As a methodological aside, we discuss the (in-)significance of conclusions being drawn from comparisons done on small sized datasets.

given this SVD, write the j th projection matrix as where T j ∈ R m×m is a diagonal matrix such that Finally, we note that the sum of projection matrices can be expressed as eigenvectors of matrix M , i.e.


For instance, methods designed for measuring semantic similarity of WordNet synsets (Banerjee and Pedersen, 2002;Budanitsky and Hirst, 2006;Pilehvar et al., 2013) usually leverage lexicographic or structural information in this lexical resource.

tors by outperforming cosine in most cases.


Given that the amount of contextual information gathered for a concept can be small, the resulting word-based vector can be sparse and as a consequence prone to noise, especially in the case of less frequent concepts.

given a pair of concepts, we first use the procedure described in Section 2 to obtain for each concept the two corresponding vector representations, i.e., word-based and synset-based.


In our setting we are only interested in the positive specificity, i.e., the set of most relevant words appearing in the contextual information.

our approach combines knowledge from both resources, providing two advantages: (1) more effective measurement of similarity based on rich semantic representations, and (2) the possibility of measuring cross-resource semantic similarity, i.e., between Wikipedia pages and WordNet synsets.


In computational biology, edit costs are defined in terms of mutation probabilities, which are irrelevant to our task.

by mixing and matching corrections from different annotators, we avoid the performance underestimation described in 1.(d).


Once we have WAcc sys and WAcc base for each system, we can compare them to determine if the text has improved.

it is worth noting that this limitation does not extend to evaluation of error detection per se using such metrics.


In the non-parallel data setting, only the target sequence f is observed and the source sequence e is hidden.

other models resort to approximation techniques -for example, the fertilitybased word alignment models apply hill-climbing and sampling heuristics in order to efficiently estimate the posteriors (Brown et al., 1993) From the computed posteriors q k we collect expected counts for each event, used to construct the M-step optimization objective.


(2008) propose PostCAT which uses Posterior Regularization (Ganchev et al., 2010) to enforce posterior agreement between the two models.

the only extra work is in the Mstep, which optimizes a single (concave) objective function.


The corpus contains a total number of 2,166 dialogues, including 15,453 utterances (10,571 for selftraining and 4,882 for testing).

in our approach, we parse all ASR-decoded utterances in our corpus using SEMAFOR 5 , a stateof-the-art semantic parser for frame-semantic parsing (Das et al., 2010;Das et al., 2013), and extract all frames from semantic parsing results as slot candidates, where the LUs that correspond to the frames are extracted for slot filling.


This proves that inter-slot relations help decide a coherent and complete slot set and enhance the interpretability of semantic slots.

from the knowledge management perspective, empowering the system with a large knowledge base is of crucial significance to modern spoken dialogue systems.


Gaussier (1999) pointed out that some lexical derivations involve character-level alternations, e.g., "c" and "ç. "

these types of pairs originally existed in S Seed but were amplified by LEXVAR.


However, in general, these works use supervised learning frameworks (Popescu et al., 2011;Ritter et al., 2012), and/or they use either a coarse representation of events, which reduces to topic modeling or classification of entire tweets (Popescu et al., 2011;Becker et al., 2011;Ritter et al., 2012), or a simplified representation of events with few arguments (Sakaki et al., 2010;Popescu et al., 2011;Benson et al., 2011;Ritter et al., 2012).

for example, for a particular earthquake, the USGS reports a depth of 22 km., while NOAA reports 25 km 2 .


The key idea is to explicitly search for concrete treebanks which are used to train parsing models.

the crucial factor here is to define N(D i ) and f D i (D).


Le and Zuidema (2014)'s reranker is an exception among supervised parsers because it employs an extremely expressive model whose features are ∞order 2 .

it is worth noting that, in this case, each phase with iterated reranking could be seen as an approximation of hard-EM (see Equation 2) where the first step is replaced by instead of searching over the treebank space, the search is limited in a neighbour set N(D i ) generated by k-best parser P i .


The tradeoff is that feature embeddings must be recomputed for each set of feature templates, unlike word embeddings, which can simply be downloaded and plugged into any NLP problem.

in general, we find that the hyperparameters that yield good word embeddings tend to yield good feature embeddings too.


The MEN-3k dataset is crowd-sourced and contains much diversity, with word pairs evidencing similarity as well as relatedness.

our work, which tackles the stronger form of lexical ambiguity in polysemy falls into the latter two of three categories.


Additionally EM+RETRO is more powerful, allowing to adapt more expressive models that can jointly learn other useful parameters -such as context vectors in the case of skip-gram.

this allows the different neighborhoods of each sense-specific vector to tease it apart from its sense-agnostic vector.


Our findings suggest several avenues for future research.

an important consideration is the relations we use and the weights we associate with them.


Since Brown clusters are mostly syntactic/semantic in nature and do not automatically distinguish positive or negative sentiment, we additionally performed multiple experiments to use clusters while incorporating additional sentiment information: On one hand, we try to incorporate the judgements on the Amazon near-domain dataset more directly into the clusters by using the repeated bisecting K-Means algorithm as implemented in CLUTO (Zhao and Karypis, 2005), with previous/next word, part-of-speech tag, and the score of the containing review as features.

(2010), and Emerson and Declerck (2014).


The four topics correspond to vision, neural network, speech recognition and electronic circuits respectively.

existing topic models assume words are generated independently and lack the mechanism to utilize the rich similarity relationships among words to learn coherent topics.


The similarity is computed based on statistics such as co-occurrence which are unable to accommodate the subtlety that whether two words labeled as similar are truly similar depends on which topic they appear in, as explained by the aforementioned examples.

to generate a document d, PtM first samples a topic proportion vector, then for each word w in d, samples a topic indicator z and generates w from the topic-word multinomial corresponding to topic z.


Since we tag entire phrases at once, we can easily assign each word in the phrase to one of these four entity-relative positions.

this reflects a plausible training scenario, with train and dev drawn from the same pool, but with distinct tests drawn from later in time.


This result is far better than the twenty hours required by SLDA to train on TRIPADVISOR.

similarly, in the TRIPADVIsOR data, both ANCHOR and sUP AN-CHOR share topics about specific destinations, but only sUP ANCHOR discovers a topic describing "disgusting" hotel rooms.


Unsurprisingly, unsupervised models (LDA) produce the best topic quality.

for clarity, we pruned words which appear in more than 3000 documents as these words appear in every topic.


Other words that were near the convex hull boundary in the unaugmented representation may become anchor words in the augmented representation because they capture both topic and sentiment ("anti-lock" vs. "lemon").

some of the original anchor words will remain, and some will be replaced by sentiment-specific anchor words.


It also has a test set, but its annotation is not made public.

both representation and evaluation are now reasonably efficient.


We thus adopted a novel approach to evaluation by simulating a grounded learning scenario using the GENIA event extraction dataset (Kim et al., 2009).

(In fact, the original distant supervision algorithm is exactly equivalent to this form, with κ = ∞.)


which are expensive and time-consuming to acquire (Zelle and Mooney, 1993;Zettlemoyer and Collins, 2005;Zettlemoyer and Collins, 2007).

future directions include: PubMed-scale pathway extraction; application to other domains; incorporating additional complex states to address syntax-semantics mismatch; learning vector-space representations for complex states; joint syntacticsemantic parsing; incorporating reasoning and other sources of indirect supervision.


Table 1 shows GUSPEE's results on GENIA event extraction.

annotating example sentences is expensive and time-consuming.


If the minimum subtree contains NULL, either in an event or argument state, it signifies a non-event and would be ignored.

the sentence also discloses important contextual information, i.e., BCL regulates RFLAT by stimulating the inhibitive effect of IL-10, and likewise the inhibition of RFLAT by IL-10 is controlled by BCL.


The magnitude indicates the strength of the association.

the Arabic sentiment system will benefit from extended sentiment lexicons and features derived specifically for the Arabic language.


Furthermore, many sentiment resources essential to automatic sentiment analysis (e.g., sentiment lexicons) exist only in English.

details of the system are described in Section 6.


We use the continuous bag-of-words model introduced by (Mikolov et al., 2013), and the tool word2vec 2 to obtain the word embeddings.

the results show that systems with better ROUGE-2 value indeed can assign higher weights to correct bigrams, allowing the ILP decoding process to select these bigrams, which leads to a better sentence selection.


Second, when we use the features from only one external resource, the results from some resources are competitive compared to that from the system using internal features.

second, to estimate the bigrams' weights, in addition to using information from the test documents, such as document frequency, syntactic role in a sentence, etc., we utilize a variety of external resources, including a corpus of news articles with human generated summaries, Wiki documents, description of name entities from DBpedia, WordNet, and sentiWordNet.


By the constraints of the algorithm, a head word x h must combine with each of its left and right dependents.

yet it appears to be completely unpredictable which will be preferred by a particular subcommunity or used in a particular application.


We have two ways to use the score values: 1) Augumenting the feature vector φ(u, v) with these scores.

we also have a combined system (KnowComb), which uses the schema knowledge to add features for learning as well as constraints for inference.


To be brief, we use the shorthand notation ) is a binary vector of size three.

these results show that our additional Predicate Schemas do not harm the predictions for regular mentions.


(In our experiments, we use a 2B words corpus and a 100k vocabulary.)

the neural language models we used to report results throughout this paper are roughly 400 MB in size.


Nowadays, it is becom- ing more and more common for these devices to include reasonably powerful GPUs, supporting the idea that further scaling is possible if necessary.

for some word w i in a given corpus, let h i denote the conditioning context w i−1 , .


The most popular language model implementation is a back-off n-gram model with Kneser-Ney smoothing (Chen and Goodman, 1999).

when the n-gram model is removed, class factored models perform better (at least for fr→en and en→de), despite being only an approximation of the fully normalised models.


(2012) propose a joint language/perception model to learn attribute names in a physical environment.

several directions of future work are very promising.


Previous approaches for semantic interpretation include domain-specific grammars (Lemon et al., 2001) and open-domain parsers together with a domain-specific lexicon (Rosé, 2000).

this definition of the description vector relies upon the structure of the domain by factorizing the attributes of entities.


KNOW-BOT starts with an empty dialog-level knowledge graph (dKG).

a mixed-initiative strategy utilizes focused prompts (line S4 in Figure 1) to introduce potentially related concepts.


This allows us to determine which effects are due to differences in genre and which are due to having a smaller training set.

ppA is a form of frontotemporal dementia which is characterized by progressive language impairment without other notable cognitive impairment.


This suggests that standard methods of sentence segmentation for spontaneous speech can be effective on PPA speech as well.

the remaining metrics in table 3 were calculated using Lu's Syntactic Complexity Analyzer (SCA) (Lu, 2010).


Our analysis so far suggests that some syntactic units are relatively impervious to the automatic sentence segmentation, while others are more susceptible to error.

in many cases, sentences ending with VB are actually statements about the difficulty of the task, rather than narrative content; e.g., that's all i can say, i can't recall, or i don't know.


in our case parsing performance is not directly dependent on the WER but rather on the type of errors made.

since the RoboCup corpus is rather small and n-gram models are typically learned from large amounts of data, in addition we interpolated the trigram models trained solely on the in-domain RoboCup corpus each with a large background language model trained on a broadcast news corpus, i.e.


While in case of applying semantically motivated recognition grammars the WER increases, it must be noted that in cases in which no back off models were applied this is to some extent due to OOG utterances (as these yield several deletions compared to the reference data).

even in 3 The results were: interpolated/adapted LM: WER: 13.43%, F1: 71.22%, semantic grammar + interpolated/adapted LM backoff: WER: 13.85, F1: 84.6%, syntactic recognition grammar: WER: 18.98%, F1: 70.86%, syntactic recognition grammar + trigram back off: WER: 13.98%, F1: 71.27%.


n-grams, while we explore template-based grammars which can capture longdistance linguistic dependencies.

we created standard trigram language models from the written training data without making use of concurrent perceptual context information using SRILM (Stolcke, 2002).


Because it is a conditional model, LOGRESP lacks any built-in capacity for semi-supervised learning.

because ITEM-RESP has no model of the data x, it receives no benefit from unannotated data  One way to make ITEMRESP data-aware is by adding a discriminative log-linear data component (Raykar et al., 2010;Liu et al., 2012).


The two optimization problems involved in the dual are hard in general.

in training, we would like to minimize the loss function ∆ by which the model will be assessed at test time.


While we adopt a bag-of-words approach for practical reasons (memory and run-time), our multi-task framework is extensible to other methods for sentence/document representations, such as those based on convolutional networks (Kalchbrenner et al., 2014;Shen et al., 2014;Gao et al., 2014b), parse tree structure (Irsoy and Cardie, 2014), and run-time inference (Le and Mikolov, 2014).

for concreteness, throughout this paper we will use query classification as the classification task and web search as the ranking task.


Our Basic model outperforms DDN for both nouns and verbs, despite training on less data.

this could occur for example if we extracted our training data from a morphologically annotated corpus.


Where previous work leveraged large, rigid rules to span paradigms, our work is characterized by small, flexible rules that can be applied to any inflection, with features determining what rule sequence works best for each pairing of a base-form with an inflection.

rather than training on 50 and 100 tables, we train on 40 and 80, but compare the results  with the models trained on 50 and 100, respectively.


To address this difficulty through EP (section 4), we will need the ability to approximate any probability distribution p that is given by a WFSA, by choosing a "simple" distribution from a family Q.

that bookkeeping can be handled with an expectation semiring (Eisner, 2002), or simply with backpointers.


For each arc or final state a in A, we can define a feature function "How many times is a used when A accepts v?"

each message update is found by minimizing a certain KL-divergence (Minka, 2001a).


According to BP, the belief b V is the pointwise product of all "incoming" messages to V .

at a given step, we only have to compute the gradient with respect to the currently nonzero features (green nodes in Figure 2) and their immediate children (yellow nodes).


Transcriptions obtained from a third language are not only noisy because of the imperfect G2P conversion, but often also lossy, in the sense of missing some phonetic information present in the source pronunciation.

the entries that have no supplemental transliterations are removed from the test sets, which results in 2,321 and 1,226 test entries.


Since we may need to leverage information from other sources, e.g., phonemes of supplemental transliterations, each training instance can be composed of a source word, a target word, and a list of supplemental strings.

we train our generalized joint model on the graphemes of the source word, as well as on the graphemes of supplemental transliterations.


For example, all animal pairs would be treated as com-patibles, whereas 54% of them are actually incompatible.

compatibility is also central to recognizing entailment (and contradiction): Standard DSMs are of relatively little use in recognizing entailment as they treat antonymous, contradictory words such as dead and alive as highly related (Adel and Schütze, 2014;Mohammad et al., 2013), with catastrophic results for the inferences that can be drawn (antonyms are just the tip of the incompatibility iceberg: dog and cat are not antonyms, but one still contradicts the other).


These features are relics of the Hearst (1992) pattern "y such as x".

we then add contextual-features (as described in §2.1), on top of the lexical features, and train classifiers analogously.


For Skip-gram and CBOW, a 5-word window size is used to allow them to get the same amount of raw information, also words appearing 5 times or fewer are filtered out.

both full English Wikipedia and Simple English Wikipedia are used as training corpora with minimal preprocessing procedures: XML tags are removed and infoboxes are filtered out, thus yielding four models: Full English Wikipedia -CbOW(FW-CbOW), Full English Wikipedia -Skip-gram(FW-SG), Simple English Wikipedia -CbOW(SW-CbOW) and Simple English Wikipedia -Skip-gram(SW-SG).


As pointed out by Agirre et al (2009) and Levy & Goldberg (2014), relatedness may actually be measuring topical similarity and be better predicted by a bag-of-words model, and similarity may be measuring functional or syntactic similarity and be better predicted by a contextwindow model.

comparing FW-SG with SW-SG and SW-cBOW, there is almost no sign of performance gain from training using full Wikipedia instead of the much smaller Simple Wikipedia.


We therefore develop a low-resource approach that relies on sourceside dependency parses only.

a schematic representation is shown in Figure 1.


This work largely follows the methodology and experimental settings of (Mikolov et al., 2013b), while we normalize the embedding and use an orthogonal transform to conduct bilingual translation.

we seek a simple approximation in this work.


The main purpose of preordering is to find a better translation performance in fast decoding conditions.

additionally, we report results on the English-to-Hindi WMT 2014 shared task (Bojar et al., 2014a) using the data provided 2 .


The new mechanism beyond Huang and Sagae (2010) is the non-trivial dynamic programming treatment of unary actions (un x and st), which is not found in dependency parsing.

experiments on both english and Chinese treebanks show that our DP parser outperforms almost all other parsers except of Carreras et al.


To alleviate the propagation of errors from part-of-speech tagging, we also extend the parser to take a tag lattice instead of a fixed tag sequence.

we first present an odd-even shift-reduce constituency parser which always finishes in same number of steps, eliminating the complicated asynchronicity issue in previous work (Zhu et al., 2013;Wang and Xue, 2014), and then develop dynamic programming on top of that.


Our implementation of the reference-based approach ("ref" in §4) uses SVR to estimate a model to predict human scores from various measures of the similarity between the response and information from the scoring guidelines provided to the human scorers.

also, the models with response-based features outperform those with just reference-based features, as observed previously by Heilman and Madnani (2013).


We treat this model as a strong baseline to which we will add reference-based features.

we use the following information from §2: (a) sentences expressing key concepts that should be present in correct responses, and (b) small sets of exemplar responses for each score level.


Turkers are presented with a timeline consisting of five consecutive days' article summaries and four variations of the accompanying comment summary:  Table 4: ROUGE-2 (R-2) and ROUGE-SU4 (R-SU4) scores (multiplied by 100) for different timeline generation approaches on four event datasets.

the full objective function consists of the three parts discussed above: Furthermore, using the following notation, we can show a closed form solution to Equation 4 as follows: Basic Features Social Features -num of words -avg/sum frequency of -absolute/relative position words appearing in comment -overlaps with headline -avg/sum frequency of -avg/sum TF-IDF scores dependency relations -num of NEs appearing in comment -TF/TF-IDF simi with comments -contains URL -JS/KL divergence (div) with article -user rating (pos/neg) -JS/KL div with comments Sentiment Features -num /proportion of positive/negative/neutral words (MPQA (Wilson et al., 2005), General Inquirer (Stone et al., 1966)) -num /proportion of sentiment words  Now we present an optimization framework for timeline generation.


special thanks to Bharat Ambati, Lea Frermann, and Daniel Renshaw for their help with system evaluation.


We observe that SceneSum summaries are overall more informative compared to those created by the baselines.

examples of the features we used for the classification task include the barycenter of a character (i.e., the sum of its distance to all other characters), PageRank (Page et al., 1999), an eigenvectorbased centrality measure, absolute/relative interaction weight (the sum of all interactions a character is involved in, divided by the sum of all interactions in the network), absolute/relative number of sentences uttered by a character, number of times a character is described by other characters (e.g., He is a monster or She is nice), number of times a character talks about other characters, and type-tokenratio of sentences uttered by the character (i.e., rate of unique words in a character's speech).


We interpret the term scene in the screenplay sense.

aMT participants are able to answer more questions regarding the story of the movie when reading SceneSum summaries.


In both cases, the graphs are constructed based on surface text; it is not a representation of propositional semantics like AMR.

given an initial step size η, the update for β on iteration t is: generation from AMR-like representations has received some attention, e.g., by Langkilde and Knight (1998) who described a statistical method.


Though we know of work in progress driven by the goal of machine translation using AMR, there is currently no system available.

our work operates on semantic graphs, taking advantage of the recently developed AMR Bank.


In total, we have 96 summaries (for more details, see B&L).

an exception is the unsupervised model of Guinaudeau and Strube (2013) (G&S), which converts the document into a graph of sentences, and evaluates the text coherence by computing the average out-degree over the entire graph.


For a given pair of entities in the text, the chance is rather low to find instances in the knowledge bases where the two arguments perfectly match the pair of entities, because entities in the source document might appear in aliases or abbreviations.

we then use the frequencies of these distribution patterns over the entire document as additional features into the entity-based model.


In a well-written document, sentences are organized and presented in a logical and coherent form, which makes the text fluent and easily understood.

moreover, for sentence ordering, world knowledge is shown to be especially useful on short documents.


To the best of our knowledge, the only exception is the unsupervised method proposed by G&S, which transforms the entity grid into a sentence graph and measures text coherence by computing the average out-degree of the graph.

we incorporate world knowledge into two existing frameworks: (1) the unsupervised graph-based model (G&S), and (2) the supervised entity-grid model (B&L).


Somewhat unfortunately, simplifying assumptions have to be made when a sentence containing multiple noncoreferent event mentions is encountered.

feature 1 encodes whether c t and e t , the trigger words of c and e, satisfy any of the following three conditions: 1. c t and e t are lexically identical; 2. c t and e t contain the same basic verb (BV) and their verb structures are compatible; 3. the similarity between c t and e t is greater than a certain threshold (which we set to 0.8 in our experiments).


To start the induction process, we initialize all parameters with uniform values.

further make the simplifying assumption that event coreference chains are all and only those coreference chains that involve at least one verb.


Since logical connections between relations are modeled explicitly, such approaches are generally hard to scale.

we present two techniques for injecting logical background knowledge, pre-factorization inference ( §3.1) and joint optimization ( §3.2), and demonstrate in subsequent sections that they generalize better than direct logical inference, even if such inference is performed on the predictions of the matrix factorization model.


Towell and Shavlik (1994) introduce Knowledge-Based Artificial Neural Networks whose topology is isomorphic to a knowledge base of facts and inference formulae.

joint optimization leads to low-rank logic embeddings that outperform all other methods in the 0 to 30% Freebase training data interval.


We plan to combine collaborator and coherence methods into a unified approach, and to use edge labels in knowledge networks for context comparison (note that the last of these is quite challenging due to normalization, polysemy, and semantic distance issues).

if an AMR parse includes no time information, we use the document creation time as an additional collaborator for mention in question.


Highly weighted term-match features are then used to find a decoding path that gives highest score to the document that is optimal with respect to both relevance and translational adequacy.

the sentences do not agree on a common set of documents.


Other CLIR approaches such as probabilistic structured queries (Darwish and Oard, 2003;Ture et al., 2012b) try to mitigate this early disambiguation by keeping enumerated translation alternatives at retrieval time.

most of a translation's structural information is lost during retrieval, and lexical choices may not be optimal for the retrieval task.


Evaluation of segment-level machine translation metrics is currently hampered by: (1) low inter-annotator agreement levels in human assessments; (2) lack of an effective mechanism for evaluation of translations of equal quality; and (3) lack of methods of significance testing improvements over a baseline.

on the one hand, if we specify a standard error that's lower than is required, and subsequently collect more repeat assessments than is needed, we would be wasting resources that could, for example, be targeted at the annotation of additional translation segments.


Scores are sampled according to annotation time to simulate a realistic setting.

for evaluation of segment-level metrics, there is no escaping the need to boost the consistency of human annotation of individual segments.


With the increasing size of parallel corpora it has become possible to achieve very high quality translation.

we show using the other languages as additional pivots leads to the construction of better phrase tables and better translation results.


Let your eyes run rapidly over several lines of print at a time.

in our experiment, we use 343 triggers in total and for each fact there are about 38 triggers in average.


Furthermore, we carefully investigated the TAC-KBP SF 2012 ground truth corpus and find that 94.36% of the biographical facts are mentioned in a sentence containing indicative fact-specific triggers.

we compare with two successful approaches: (1) the combination of distant supervision and rules (e.g., (Grishman, 2013;); (2) patterns based on dependency paths (e.g., Yu et al., 2013)).